Location

Description

2019-04-26 9:37pm Update: From the 202 head north on Gilbert Rd to the Pecos intersection. Turn Left (West) make the first right (North) to enter the Campus. Parking lots on the left. Lot 5 has buildings JAV GIL and JAC to the north. Cholla is behind the building marked EST. EST is on the west side of Lot 5.

Lecture Hall Cholla 110 for main session. Breakouts and Hands on Labs will be in 102-107 throughout the day. Park in lot 5 for closest access to Cholla that is just behind Estrella and Saguaro Halls.

Cholla 102-107 are on the ground level facing the Estrella Courtyard. Cholla 110 is in the hallway on the south side of the building.

On April 27, 2019, all communities will come together once again in the seventh great Global Azure Bootcamp event! We are one of many user groups organizing our own one-day deep dive class on Azure the way it works best for our Phoenix members. The result is that thousands of people get to learn about Azure and join together online under the social hashtag #GlobalAzure!

Logistics:

Get there in person, bring your laptop for hands on labs, get ready for fun and engagement.

Featured Speakers:

Ginger Grant: Microsoft Data Platform MVP @desertisleSQL

Ginger Grant manages the consultancy Desert Isle Group and shares what she has learned while working with data technology to people around the world. Last year she co-authored the book Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learningand has recently released an online class at Datacamp on Intermediate T-SQL. As a Microsoft MVP in Data Platform, Microsoft Certified Trainer and an Idera ACE, she focuses on guiding clients to create solutions using the entire Microsoft Data Stack, which includes SQL Server, Power BI and Azure Data Cloud components. When not working, she protypes the latest pre-release data technologies, maintains her blog http://www.desertislesql.com, spends time on twitter @desertislesql.

Abstracts:

Data Driven Decision Making with Azure Machine Learning

Azure Machine Learning [ML] is a great tool for providing deep analytical data analysis and can provide a great learning environment to those people who are just getting started with learning machine learning concepts as well as those who want to deploy complex models created in R or Python. This session will introduce how to use Azure ML to process data in any part of your data environment, where data is either stored in the cloud or in an on-premise in SQL Server. If you are interested in learning data science, this session will show you how you can use Azure ML to help understand the different algorithm classifications of used in data science. This session will also cover the different methods of deploying a production solution with Azure ML which can scale to handle streaming or batch data processing requirements with and Azure Data Factory Deployment or handling a small sample set of data in Excel.

Learning Objectives

Understand how to use Azure Machine Learning to provide a deep analysis of data for tasks such as predictive and regression analysis.

Learn the use cases for determining when you might want to use Azure Databricks.Learn how to integrate this analysis in your current data environment with Azure components such as Azure Data Factory and other methods.

Using Azure Databricks to develop scalable data solutions

Worried about performance when processing a Data Lake or other large data store? Need to develop in a collaborative environment and visualize the results in Power BI? Azure Databricks, one of the newer components added to Azure, allows users to connect to data sources such as Azure Data Lake, Azure Blob Storage, Azure SQL DW, Cosmos DB, Azure DB, and SQL Server and stream the data using Apache Spark for processing data to create a machine learning [ML] solution and providing the data to Power BI for visualization. Azure Databricks can provide a very quick way of processing data by adding nodes increase performance for tasks, such as analyzing data for a ML solution from an Azure data store. Azure Databricks also includes a collaborative workspace so that using Azure Active Directory, teams of people can create code in a notebook in R or Python and implement the notebook as an Azure Databrick job. The step-by-step demos will include all you need to know to implement Databricks.

Learning Objectives

Azure Databricks provides the ability to use massive scale to read and stream data to create ML solutions to provide insight for large data stores.

Provide methods for learning how to implement complex algorithms either natively in Azure ML or in R or Python

Gain an understanding of what Azure Databricks does and how to implement it.

Understand how you can use SQL to get data for analysis and implement streaming data with Apache Spark to run a machine learning solution and scale it to process large datasets quickly.

Azure Logic Apps 101 Hands-On

This entry-level presentation is designed to discuss the basics of Logic Apps, its uses, and how to develop them. Join us for a hands-on lab where you will build a Logic App to perform a data movement scenario.

Requirements:Bring a laptop computer to work on the lab.

Azure Serverless Architecture

We will discuss Azure serverless offerings (Functions and Logic Apps), and how to use them together to build compelling solutions. We have some cool demos to show, don't miss it.

Azure IaaS with Citrix

Azure IaaS componentes and design. How to deliver Desktops as an Internal Service. How to solve application performance, compatibility, and browser management issues. Also hands on labs.

Azure Netwroking and Containers

We will discuss Azure Kubernetes, networking, integration with compute and IaaS. Also hands on labs.

If you are interested in any of these topics or interested in presenting on these topics reach out to roy.tokeshi@citrix.com 602.614.4931